1. Field of the Invention
The present invention relates to a method and an apparatus for diagnosing a plant using a plant model in an abstract function level based on a human cognitive process so as to support countermeasures and decisions of an operator of the plant against an anomaly thereof.
2. Description of the Related Art
In the operation of a large-scaled, complicated industrial process plant such as a nuclear power plant and petrochemical plant, even a tiny malfunction in a local region of the plant should be early detected and proper countermeasures should be taken so as to prevent the malfunction to propagate to the entire plant, and keep plant operation safe and economical. Thus, a large number of instruments for monitoring plant operational states have been installed and alarm generating functions have been provided to most of them so as to rapidly detect such a malfunction or anomaly.
However, it is difficult for the operators to rapidly and properly take countermeasures against many alarms. In addition, it is not practical to measure and monitor the states of all elements of the plant.
To solve such a problem, a variety of plant diagnosing apparatuses that diagnose operating states of plants corresponding to measured signals of these plants and support monitoring/diagnosing operations of the operators have been developed. In a typical example of such apparatuses, all anomalies that may take place are assumed beforehand and transient change patterns of typical plant state amounts for such anomalies are stored beforehand. When plant transients take place, they are compared with the stored patterns so as to detect the type of the anomaly. A diagnosis using a neural network can be considered as a method of which typical patterns are stored in connections of the neuron network.
In a large-scaled complicated plant, various abnormal events take place. Thus, it is difficult to prepare progress patterns of these events beforehand. In addition, even if such an event takes place, it differs from a pre-considered pattern in details. Many cases in other fields show that a serious trouble often results from many troubles that unexpectedly take place at a time.
In addition, since the operational staff should make the final decision with reference to the output from the plant diagnosing apparatus, the diagnosing process thereof should be fully understood by them. However, the human cannot memorize a huge number of combinations of the causes of anomalies and the indications for the consequence. Accordingly, from past examples of medical expert systems, it can be easily estimated that the operator cannot adequately understand the output from the plant diagnosing apparatuses.